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Class Incremental Learning

Papers

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Showing 351–375 of 634 papers

TitleStatusHype
DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning—0
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning—0
Dual Embodied-Symbolic Concept Representations for Deep Learning—0
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning—0
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Dynamic Feature Learning and Matching for Class-Incremental Learning—0
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning—0
Dynamic Prompt Adjustment for Multi-Label Class-Incremental Learning—0
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning—0
Effective Decision Boundary Learning for Class Incremental Learning—0
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Efficient Non-Exemplar Class-Incremental Learning with Retrospective Feature Synthesis—0
Elephant Neural Networks: Born to Be a Continual Learner—0
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning—0
Endpoints Weight Fusion for Class Incremental Semantic Segmentation—0
Energy Aligning for Biased Models—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning—0
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks—0
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach—0
Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse—0
Enhancing Visual Continual Learning with Language-Guided Supervision—0
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation—0
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All datasetscifar100CIFAR-100 - 50 classes + 10 steps of 5 classesCIFAR-100 - 50 classes + 5 steps of 10 classesCifar100-B0(10 tasks)-no-exemplarsCifar100-B0(20 tasks)-no-exemplarsCIFAR100-B0(50 tasks)-no-exemplars

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1S&B10-stage average accuracy68.18—Unverified
2SCR10-stage average accuracy65.98—Unverified
3iCaRL10-stage average accuracy63.24—Unverified
4LUCIR10-stage average accuracy56.53—Unverified
5ABD10-stage average accuracy54.44—Unverified
6EWC10-stage average accuracy50.53—Unverified
7EMR10-stage average accuracy48.66—Unverified
8A-GEM10-stage average accuracy45.76—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy61.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy56.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy42.6—Unverified